Infrared spectroscopy as a useful tool to predict land use depending on Mediterranean contrasted climate conditions: A case study on soils from olive-orchards and forests

摘要

Soil chemical properties depend on various environmental factors such as above ground vegetation, climate and the parent rock substratum. Land use, and the associated management practices, is one of the major drivers which can deeply impact soil properties. To better understand the dynamics of soil chemical properties and to assess potential impact of land use, an improved monitoring of chemical signature in organo-mineral topsoils is necessary. Here, we explored how land use (forests or agrosystems i.e. olive-tree orchards) may shape soil chemical signature and whether it depends i) on the type of agricultural or sylvicultural practices, ii) on contrasted Mediterranean climate conditions at different spatial scales. We measured variations in soils properties by FTIR-ATR (Fourier-Transformed Infrared - Attenuated Total Reflectance) spectroscopy and elemental concentrations. FTIR showed that the aromatic fraction of organic matter and CaCO3 discriminated soils under different land uses (orchards or forests) and this depended on climate (sub-humid vs humid climate). Moreover, the chemical signatures of soils varied with the practices applied. For agrosystems, soils complemented with olive-mill wastes were characterized by aromatics compared to soils under natural grass or tillage. For forests, soils from Pious spp. stands and Quercus spp. stands were discriminated by CaCO3 and aromatics respectively. Contrasted climate conditions at local scale, i.e. northern vs southern slopes for forests and distance from the sea (coastal vs inland area) for agrosystems, had an effect on soil chemical signature. The AcomDIM interpretation of FFIR-ATR signals showed that factors land use", "practices" and "climate" and their interactions could have a significant impact on soil chemical signature. PLS modeling also confirmed that FTIR-ATR is a useful tool to predict a type of land use depending on climate. (C) 2019 Elsevier B.V. All rights reserved.